Multimodal neuroimaging data is abundantly available and although approaches that seek to combine these data, e.g., JointICA [1]}, and more recently multimodal subspace analysis [2]} focus on linear decompositions, recent work on multimodal deep learning offers the benefits of additional flexibility which can also capture nonlinear relationships. Multimodal deep learning research mostly focuses on the relationship between audio, images, and/or text [3]}. The exciting new direction of multimodal representation learning, together with growing evidence that deep learning representations can provide robust biomarkers [4]}, paves the way for multimodal representation learning in neuroimaging.
